Time series data are time-stamped data collected over time. Some examples of time series data are                web visits per hour        sales per month        inventory draws per week        calls per day        trades per weekday        etc.        
As can be seen, the frequency associated with the time series varies with the problem at hand. The frequency or time interval may be hourly, daily, weekly, monthly, quarterly, yearly, or many other variants of the basic time intervals.
Associated with a time series could be a seasonal cycle (seasonality) or a business cycle. For example, the length of seasonality for a monthly time series is usually assumed to be twelve because there are twelve months in a year. Likewise, the seasonality of a daily time series is usually assumed to be seven. The usual seasonality assumption may not always hold. For example, if a particular business' seasonal cycle is fourteen days long, the seasonality is fourteen, not seven. Seasonality considerations constitutes just some of the difficulties confronting analysis of a time series. The difficulties significantly grow if many time series have to be analyzed.